Tarea 3

# Carga de datos
 primates_cr_registros <-
  st_read(
    "https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/gbif/primates-cr-registros.csv",
    options = c(
      "X_POSSIBLE_NAMES=decimalLongitude",
      "Y_POSSIBLE_NAMES=decimalLatitude",
    quiet = TRUE
  )
  )

## options:        X_POSSIBLE_NAMES=decimalLongitude Y_POSSIBLE_NAMES=decimalLatitude TRUE 
## Reading layer `primates-cr-registros' from data source `https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/gbif/primates-cr-registros.csv' using driver `CSV'
## Simple feature collection with 4509 features and 50 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -85.96248 ymin: 8.040197 xmax: -82.55949 ymax: 11.15408
## CRS:           NA

# Asignacion de CRS
st_crs(primates_cr_registros) = 4326

# Capa geoespacial de cantones 
cantones <- 
  st_read("https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/ign/delimitacion-territorial-administrativa/cr_cantones_simp_wgs84.geojson",
              quiet = TRUE
  )

# Capa geoespacial de provincias
provincias <-
  st_read("https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/ign/delimitacion-territorial-administrativa/cr_provincias_simp_wgs84.geojson",
    quiet = TRUE
  )

# Carga de capa de altitud
alt <- getData(
  "worldclim",
  var = "alt",
  res = .5,
  lon = -84,
  lat = 10
)
altitud <-
  alt %>%
  crop(provincias) %>%
  mask(provincias)

# Carga de capa de color
rcol <- colorNumeric(c("#98FB98", "#808000", "#66CDAA"),
                     values(altitud),
                     na.color = "transparent")

# Cruce espacial con la tabla de cantones, para obtener el nombre del cantón
primates_cr_registros <- 
  primates_cr_registros %>%
  st_join(cantones["canton"])

# Tabla de registros de presencia
primates_cr_registros%>%
  st_drop_geometry() %>%
  select(stateProvince,canton,family, species, eventDate) %>%
  datatable(
    colnames = c("Provincia", "Canton", "Especie", "Familia", "Fecha"),
    options = list( searchHighlight = TRUE,language = list(url = '//cdn.datatables.net/plug-ins/1.10.11/i18n/Spanish.json')
    )
  )

## Filtro para contar especies
mono_ardilla <-  primates_cr_registros %>% 
  dplyr::select(stateProvince,canton,family, species, eventDate) %>%
  filter(species == "Saimiri oerstedii")
mono_aullador <-  primates_cr_registros %>% 
  dplyr::  select(stateProvince,canton,family, species, eventDate) %>%
  filter(species == "Alouatta palliata")
mono_carablanca <-  primates_cr_registros %>% 
  dplyr::  select(stateProvince,canton,family, species, eventDate)%>%
  filter(species == "Cebus capucinus")
mono_arana <-  primates_cr_registros  %>%
  dplyr::select(stateProvince,canton,family, species, eventDate)%>%
  filter(species == "Ateles geoffroyi Kuhl")

## Grafico pastel
primates_cr_registros%>%
  plot_ly(
    labels = ~c("mono_ardilla","mono_aullador","mono_carablanca","mono_arana"
    ),
    values = ~c(453, 1994, 1463, 599),
    
    type = "pie")%>%
  config(locale = "es")%>%
  layout(
    title = "",
    xaxis = list(showgrid = FALSE,zeroline = FALSE,showticklabels = FALSE
    ),
    yaxis = list( showgrid = FALSE,zeroline = FALSE, showticklabels = FALSE
    )
  )

au <- primates_cr_registros %>%
  dplyr::select(species,
                stateProvince,
                canton,
                eventDate) %>%
  filter(species == "Alouatta palliata")

ard <- primates_cr_registros %>%
  dplyr::select(species,
                stateProvince,
                canton,
                eventDate) %>%
  filter(species == "Saimiri oerstedii")

ara <- primates_cr_registros %>%
  dplyr::select(species,
                stateProvince,
                canton,
                eventDate) %>%
  filter(species == "Ateles geoffroyi")

cara <- primates_cr_registros %>%
  dplyr::select(species,
                stateProvince,
                canton,
                eventDate) %>%
  filter(species == "Cebus capucinus")

mau <- paste0((au$species),
              (au$stateProvince),
              (au$canton),
              (au$eventDate))

mard <- paste0((ard$species),
               (ard$stateProvince),
               (ard$canton),
               (ard$eventDate))
mara <- paste0((ara$species),
               (ara$stateProvince),
               (ara$canton),
               (ara$eventDate))
mcara <- paste0((cara$species),
                (cara$stateProvince),
                (cara$canton),
                (cara$eventDate))
primates_cr_registros %>%
  leaflet() %>%
  addProviderTiles(providers$OpenStreetMap.Mapnik, group = "OpenStreetMap") %>%
  addProviderTiles(providers$Stamen.TonerLite, group = "Stamen Toner Lite") %>%
  addProviderTiles(providers$Esri.WorldImagery, group = "Imágenes de ESRI") %>%
  addRasterImage(
    altitud, 
    colors = rcol, 
    opacity = 0.8,
    group = "Altitud") %>%
  addCircleMarkers(
    data = au,
    stroke = F,
    radius = 4,
    fillColor = "#FFA07A",
    fillOpacity = 1,
    popup = mau,
    group = "Alouatta palliata"
  ) %>%
  addCircleMarkers(
    data = ard,
    stroke = F,
    radius = 4,
    fillColor = "#FFA500",
    fillOpacity = 1,
    popup = mard,
    group = "Saimiri oerstedii"
  ) %>%
  addCircleMarkers(
    data = ara,
    stroke = F,
    radius = 4,
    fillColor = "   #4B0082",
    fillOpacity = 1,
    popup = mara,
    group = "Ateles geoffroyi"
  ) %>%
  addCircleMarkers(
    data = cara,
    stroke = F,
    radius = 4,
    fillColor = "   #00FF00",
    fillOpacity = 1,
    popup = mcara,
    group = "Cebus capucinus"
  ) %>%
  addLayersControl(
    baseGroups = c("OpenStreetMap", "Stamen Toner Lite", 
                   "Imágenes de ESRI"),
    overlayGroups = c("Mono Aullador", "Mono Ardilla", 
                      "Mono Araña", "Mono Carablanca"
                      ,"Altitud")
  ) %>%
  addMiniMap(tiles = providers$Stamen.OpenStreetMap.Mapnik,
             position = "bottomleft",
             toggleDisplay = TRUE
  )